DESIGN OF RANSOMWARE DETECTION BEHAVIOUR MODEL ON NETWOK USING MACHINE LEARNING - Satria Yudha, Sarlendra
Data from the McAfee Labs Threats Report states that in the first quarter of 2019, Ransomware attacks increased by 118%. The impact of a Ransomware attack caused significant financial losses to many victims, including organizations, health facilities, and individuals. This happens because ransomware is currently being used as a bad tool, namely to earn money, steal data, hack systems or stop normal functioning of the system. Ransomware has characteristics and behavior in carrying out attacks, namely by infecting the victim's computer. Then, the ransomware will contact the C&C (Command and Control) server to obtain or store the encryption key. After getting the encryption key from the C&C or it can be locally, the encryption key management process will then occur, which is the process of saving the encryption key to a remote server. After that the ransomware will encrypt the system and perform blackmail on the victim. Currently ransomware Detection is mostly done by host based methods. However, the host based method requires that the infected host first then detects it. This is dangerous, as the ransomware can encrypt files in just minutes. For that we need an accurate model in detecting ransomware, where the Detection occurs before the ransomware performs any destructive action. In this study, the aim of this research is to design a model that can detect several Ransomware families based on their behavior in the network. This model is designed to be able to detect ransomware accurately and early. This can be done by extracting behavioral features from different ransomware families. These features are the behavior of the ransomware that is being carried out, until before the encryption phase. Then the model will use these features to conduct training. From the test results, this study compares several algorithms such as SVM, Decision Tree, KNN, Naïve Bayes and Random Forest. Then, the best accuracy is Naïve Bayes, with detection accuracy reaching 89% on cross validation and 95% on confusion matrix.